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Sports Medicine

, Volume 41, Issue 12, pp 1003–1017 | Cite as

Neural Network Modelling and Dynamical System Theory

Are They Relevant to Study the Governing Dynamics of Association Football Players?
  • Aviroop Dutt-MazumderEmail author
  • Chris Button
  • Anthony Robins
  • Roger Bartlett
Review Article

Abstract

Recent studies have explored the organization of player movements in team sports using a range of statistical tools. However, the factors that best explain the performance of association football teams remain elusive. Arguably, this is due to the high-dimensional behavioural outputs that illustrate the complex, evolving configurations typical of team games. According to dynamical system analysts, movement patterns in team sports exhibit nonlinear self-organizing features. Nonlinear processing tools (i.e. Artificial Neural Networks; ANNs) are becoming increasingly popular to investigate the coordination of participants in sports competitions. ANNs are well suited to describing high-dimensional data sets with nonlinear attributes, however, limited information concerning the processes required to apply ANNs exists. This review investigates the relative value of various ANN learning approaches used in sports performance analysis of team sports focusing on potential applications for association football. Sixty-two research sources were summarized and reviewed from electronic literature search engines such as SPORTDiscus™, Google Scholar, IEEE Xplore, Scirus, ScienceDirect and Elsevier. Typical ANN learning algorithms can be adapted to perform pattern recognition and pattern classification. Particularly, dimensionality reduction by a Kohonen feature map (KFM) can compress chaotic high-dimensional datasets into low-dimensional relevant information. Such information would be useful for developing effective training drills that should enhance self-organizing coordination among players. We conclude that ANN-based qualitative analysis is a promising approach to understand the dynamical attributes of association football players.

Keywords

Artificial Neural Network Artificial Neural Network Model Team Sport Dynamical System Theory Artificial Neural Network Architecture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

No sources of funding were used to assist in the preparation of this review. The authors have no conflicts of interest that are directly relevant to the content of this review. The authors would like to thank Gavin Kennedy for his input into the article as part of their research discussion group.

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Copyright information

© Adis Data Information BV 2011

Authors and Affiliations

  • Aviroop Dutt-Mazumder
    • 1
    Email author
  • Chris Button
    • 1
  • Anthony Robins
    • 2
  • Roger Bartlett
    • 1
  1. 1.School of Physical EducationUniversity of OtagoDunedinNew Zealand
  2. 2.Department of Computer ScienceUniversity of OtagoDunedinNew Zealand

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